17th International Mass Spectrometry Conference :: Prague, 2006
> Go to contents (site navigation)
|Presentation date:||Mon, Aug 28, 2006|
|Presentation time:||09:50 – 11:20|
Scott M. Peterman1, Manor Askenazi2, David V. Huhman3, Stacey Allen3, Lloyd W. Sumner31 Thermo Electron Corporation, Somerset, United States
Correspondence address: Scott M. Peterman, Thermo Electron Corporation, 265 Davidson Ave., Suite 101, Somerset, NJ, 08873 United States.
Keywords: Collision-Induced Dissociation (CID); Data Analysis; Mass Spectrometry, Fourier Transform; Metabolic Profiling.
Novel aspect: Metabolomics studies using accurate mass MSn for identification and characterization on one mass spectral platform.
Legumes are economically important agricultural crops and possess a vast array of secondary metabolites that are associated with human/animal nutrition and health. Plant metabolic profiling however, presents many challenges for identification and characterization using mass spectrometry due to the complexity of the metabolome. Specifically, saponin structures facilitate multiple sites of modifications which increase the complexity of the resulting metabolome. In addition, the chemical formulae for saponins and monosaccharides primarily differ only by the oxygen content resulting in many possible structures for each metabolite. Our approach is to utilize accurate mass MSn analysis for identification and characterization of metabolized saponins. Using accurate mass product ion filtering increases the characterization step by dramatically reducing false positives for aglycon and monosaccharide identification.
All experiments were performed on a hybrid linear ion trap/orbitrap mass spectrometer using high-resolution/high-mass accuracy data acquisition for full scan MS, MS/MS, and MS3 scan types. The scan cycle used for metabolic profiling contains six data-dependent scan events with three MS/MS and MS3, respectively. M. truncatula ecotypes were propagated from a single seed source and replicated plants were grown and controlled in an environmental growth chamber. The resulting data was statistically processed using the proprietary software package SIEVE.
Initial experiments were focused on the identification and characterization of metabolized saponins from the M. truncatula ecotype A-17 using accurate mass MSn analysis. Instrument methods using high-resolution/high-mass accuracy data-dependent MSn events generated over 5000 spectra in a 90 minute HPLC/MS analysis. In a post-acquisition manner, identification of various aglycones was achieved using a 5 ppm tolerance for product ion filtering which identified over 120 different metabolites of five base saponin structures, increasing the number identified 4-fold relative to previously published results. Further inspection of the product ion spectra revealed dissociation patterns characteristic of the type and degree of glycosylation which was confirmed using accurate mass product ion analysis. In addition, product ion spectra and corresponding retention times were used to differentiate isomeric metabolites.
Following metabolic profiling, relative expression levels were analyzed for 30 different M. truncatula ecotypes using SIEVE. Data matrices consisting of m/z, retention time, and intensity were extracted and used for further discriminate and multivariate analyses. Hierarchical and principal component analyses of the ecotypes were performed and used to evaluate relationships. Additional clustering and refining processes showing direct relationships based on the type and number of glycosylation will be presented.